This behavioral Model Context Protocol (MCP) tool addresses key limitations of AI coding assistants by introducing a robust validation layer throughout the software development lifecycle. It enforces explicit LLM evaluations at critical stages such as research, system design, planning, code changes, testing, and task completion verification. By requiring evidence-based research, promoting reuse over reinvention, facilitating human-in-the-loop decisions, and establishing quality gates for security, performance, and maintainability, it significantly elevates the reliability and quality of AI-assisted code generation, fostering safer and more maintainable software.
Key Features
01User-driven Decision Elicitation for ambiguity and blockers
02Comprehensive Plan and Design Review
03Security Validation in system design and code changes
04Staged Workflow Enforcement for plan, code, and test approvals
05Intelligent Code Evaluation via MCP Sampling
0615 GitHub stars
Use Cases
01Validating AI-generated code, plans, and tests against engineering standards and security best practices.
02Enforcing evidence-based research and promoting code reuse in AI-assisted development workflows.
03Integrating human-in-the-loop decision-making for ambiguous requirements or obstacles in AI coding tasks.
